Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2019

Abstract

Biclustering of observations and the variables is of interest in many scientific disciplines. In a single set of data matrix it is handled through the singular value decomposition. Here we deal with two sets of variables: response and predictor sets. We model the joint relationship via regression models and then apply SVD on the coefficient matrix. The sparseness condition is introduced via Group Lasso. The approach discussed here is quite general and is illustrated with an example from Finance.

Keywords

multivariate regression, singular value decomposition, dimension reduction, mixture models

Discipline

Finance and Financial Management

Research Areas

Finance; Quantitative Finance

Publication

Proceedings of the 19th International Conference, Faro, Portugal, 2019 June 12-14

First Page

533

Last Page

549

ISBN

9783030227401

Identifier

10.1007/978-3-030-22741-8_38

Publisher

Springer Verlag

City or Country

Faro, Portugal

Additional URL

https://doi.org/10.1007/978-3-030-22741-8_38

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